A neighborhood graph based approach to regional co-location pattern discovery: A summary of results

Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers, Zhe Jiang, Nicole Wayant

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations

Abstract

Regional co-location patterns (RCPs) represent collections of feature types frequently located together in certain localities. For example, RCP < (Bar, Alcohol - Crimes), Downtown >suggests that a co-location pattern involving alcohol-related crimes and bars is often localized to downtown regions. Given a set of Boolean feature types, their geo-located instances, a spatial neighbor relation, and a prevalence threshold, the RCP discovery problem finds all prevalent RCPs (pairs of co-locations and their prevalence localities). RCP discovery is important in many societal applications, including public safety, public health, climate science and ecology. The RCP discovery problem involves three major challenges: (a) an exponential number of subsets of feature types, (b) an exponential number of candidate localities and (c) a tradeoff between accurately modeling pattern locality and achieving computational efficiency. Related work does not provide computationally efficient methods to discover all interesting RCPs with their natural prevalence localities. To address these limitations, this paper proposes a neighborhood graph based approach that discovers all interesting RCPs and is aware of a pattern's prevalence localities. We identify partitions based on the pattern instances and neighbor graph. We introduce two new interest measures, a regional participation ratio and a regional participation index to quantify the strength of RCPs. We present two new algorithms, Pattern Space (PS) enumeration and Maximal Locality (ML) enumeration and show that they are correct and complete. Experiments using real crime datasets show that ML pruning outperforms PS enumeration.

Original languageEnglish (US)
Title of host publication19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011
Pages122-131
Number of pages10
DOIs
StatePublished - Dec 1 2011
Event19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011 - Chicago, IL, United States
Duration: Nov 1 2011Nov 4 2011

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011
CountryUnited States
CityChicago, IL
Period11/1/1111/4/11

Keywords

  • maximal localities
  • prevalence localities
  • regional co-location patterns
  • regional participation index
  • spatial analysis
  • spatial heterogenity

Fingerprint Dive into the research topics of 'A neighborhood graph based approach to regional co-location pattern discovery: A summary of results'. Together they form a unique fingerprint.

Cite this